Monday.com + Bruin

Source

Ingest Monday.com data into your warehouse with incremental loading, quality checks, and full lineage. Defined in YAML, version-controlled in Git.

For business teams

What you get

Operational analytics
Monday.com data in your warehouse means analytics that Monday.com's built-in reporting can't provide. Cross-tool, cross-team, custom.
Cross-tool project views
Combine Monday.com with Jira, GitHub, Slack, and other tools. One dashboard that shows the real state of projects.
Team workload insights
Understand collaboration patterns, bottlenecks, and workload distribution from Monday.com data — automatically updated.
No manual data pulling
Monday.com data syncs on schedule. Managers and leads get fresh data without asking anyone.

For data & engineering teams

How it works

Incremental sync
Only sync new and changed Monday.com records. No full reloads, no wasted compute.
YAML-defined, Git-versioned
Your Monday.com pipeline is a YAML file. Review in PRs, deploy with CI/CD, roll back with git revert.
Schema change handling
Bruin detects Monday.com schema changes automatically. No manual intervention when fields get added or renamed.
Cross-tool joins
Combine Monday.com data with other tools in SQL transforms. Bruin resolves dependencies across sources automatically.

Before you start

Monday.com account

API token from account settings

Step 1

Add your Monday.com connection

Connect using Monday.com API token. Add this to your Bruin environment file — credentials are stored securely and referenced by name in your pipeline YAML.

Parameters

api_tokenMonday.com API token for authentication

connections:
  monday:
    type: monday
    uri: "monday://?api_token=<api_token>"

Step 2

Create your pipeline

Define a YAML asset that tells Bruin what to pull from Monday.com and where to land it. This file lives in your Git repo — reviewable, version-controlled, and deployable with CI/CD.

Available tables

accountusersboardsworkspacesupdatesteamstags

name: raw.monday_account
type: ingestr

parameters:
  source_connection: monday
  source_table: 'account'
  destination: bigquery

Step 3

Add quality checks

Add column-level and custom SQL checks to your Monday.com data. If a check fails, the pipeline stops — bad data never reaches downstream models or dashboards.

Validate workspace data synced completely

Ensure record IDs are unique and titles are present

Catch missing or null fields on every sync

columns:
  - name: id
    checks:
      - name: not_null
      - name: unique
  - name: title
    checks:
      - name: not_null

custom_checks:
  - name: workspace sync is complete
    query: |
      SELECT COUNT(*) > 0
      FROM raw.monday_account

Step 4

Run it

One command. Bruin connects to Monday.com, pulls data incrementally, runs your quality checks, and lands clean data in your warehouse. If a check fails, the pipeline stops — bad data never reaches downstream.

Backfill historical data with --start-date

Schedule with cron or trigger from CI/CD

Full lineage from Monday.com to your dashboards

$ bruin run .

Running pipeline...

  monday_account
    ✓ Fetched 2,847 new records
    ✓ Quality: campaign_id not_null     PASSED
    ✓ Quality: spend not_null           PASSED
    ✓ Quality: no negative ad spend     PASSED
    ✓ Loaded into bigquery

  Completed in 12s

Other Productivity integrations

Airtable

Asana

Bitbucket

Calendly

Canny

ClickUp

Ready to connect Monday.com?

Start for free, or book a demo to see how Bruin handles ingestion, quality, lineage, and scheduling for your entire data stack.

Book a demo Read the docs